7 accepted papers
Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks…
Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes supported by only a few annotated samples. However existing FSS methods often underperform in the presence of domain shifts especially when encountering new domain styles that are unseen during tra…
Redundancy is a persistent challenge in Capsule Networks (CapsNet) leading to high computational costs and parameter counts. Although previous studies have introduced pruning after the initial capsule layer dynamic routing's fully connected nature and non-orthogonal weight matrices reintroduce redun…
As a prominent parameter-efficient fine-tuning technique in NLP, prompt tuning is being explored its potential in computer vision. Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embe…
"Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary annotation. The crux of this task lies in locating each transcription in scene text images without location annotations.…
"While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specific overfitting on base classes and sample-specific overfitting on novel classes. In this work we design a novel knowledg…
"Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-st…